Journal articles: 'Crop parameter'

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Wijesingha, Jayan, Supriya Dayananda, Michael Wachendorf, and Thomas Astor. "Comparison of Spaceborne and UAV-Borne Remote Sensing Spectral Data for Estimating Monsoon Crop Vegetation Parameters." Sensors 21, no. 8 (April 20, 2021): 2886. http://dx.doi.org/10.3390/s21082886.

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Various remote sensing data have been successfully applied to monitor crop vegetation parameters for different crop types. Those successful applications mostly focused on one sensor system or a single crop type. This study compares how two different sensor data (spaceborne multispectral vs unmanned aerial vehicle borne hyperspectral) can estimate crop vegetation parameters from three monsoon crops in tropical regions: finger millet, maize, and lablab. The study was conducted in two experimental field layouts (irrigated and rainfed) in Bengaluru, India, over the primary agricultural season in 2018. Each experiment contained n = 4 replicates of three crops with three different nitrogen fertiliser treatments. Two regression algorithms were employed to estimate three crop vegetation parameters: leaf area index, leaf chlorophyll concentration, and canopy water content. Overall, no clear pattern emerged of whether multispectral or hyperspectral data is superior for crop vegetation parameter estimation: hyperspectral data showed better estimation accuracy for finger millet vegetation parameters, while multispectral data indicated better results for maize and lablab vegetation parameter estimation. This study’s outcome revealed the potential of two remote sensing platforms and spectral data for monitoring monsoon crops also provide insight for future studies in selecting the optimal remote sensing spectral data for monsoon crop parameter estimation.

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Wallach, Daniel, Bruno Goffinet, Jacques-Eric Bergez, Philippe Debaeke, Delphine Leenhardt, and Jean-Noël Aubertot. "Parameter Estimation for Crop Models." Agronomy Journal 93, no. 4 (July 2001): 757–66. http://dx.doi.org/10.2134/agronj2001.934757x.

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Stanghellini, C., and W. Th M. van Meurs. "CROP TRANSPIRATION: A GREENHOUSE CLIMATE CONTROL PARAMETER." Acta Horticulturae, no. 245 (August 1989): 384–88. http://dx.doi.org/10.17660/actahortic.1989.245.51.

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Jacobs, Adrie F. G., and John H. Van Boxel. "Computational parameter estimation for a maize crop." Boundary-Layer Meteorology 42, no. 3 (February 1988): 265–79. http://dx.doi.org/10.1007/bf00123816.

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Tremblay, Marie, and Daniel Wallach. "Comparison of parameter estimation methods for crop models." Agronomie 24, no. 6-7 (September 2004): 351–65. http://dx.doi.org/10.1051/agro:2004033.

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T. Zhai, R. H. Mohtar, F. El-Awar, W. Jabre, and J. J. Volenec. "PARAMETER ESTIMATION FOR PROCESS-ORIENTED CROP GROWTH MODELS." Transactions of the ASAE 47, no. 6 (2004): 2109–19. http://dx.doi.org/10.13031/2013.17796.

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Manoharan, Dr Samuel. "Supervised Learning for Microclimatic parameter Estimation in a Greenhouse environment for productive Agronomics." September 2020 2, no. 3 (July 17, 2020): 170–76. http://dx.doi.org/10.36548/jaicn.2020.3.004.

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Maximum crop returns are essential in modern agriculture due to various challenges caused by water, climatic conditions, pests and so on. These production uncertainties are to be overcome by appropriate evaluation of microclimate parameters at commercial scale for cultivation of crops in a closed-field and emission free environment. Internet of Things (IoT) based sensors are used for learning the parameters of the closed environment. These parameters are further analyzed using supervised learning algorithms under MATLAB Simulink environment. Three greenhouse crop production systems as well as the outdoor environment are analyzed for comparison and model-based evaluation of the microclimate parameters using the IoT sensors. This analysis prior to cultivation enables creating better environment and thus increase the productivity and harvest. The supervised learning algorithm offers self-tuning reference inputs based on the crop selected. This offers a flexible architecture and easy analysis and modeling of the crop growth stages. On comparison of three greenhouse environment as well as outdoor settings, the functional reliability as well as accuracy of the sensors are tested for performance and validated. Solar radiation, vapor pressure deficit, relative humidity, temperature and soil fertility are the raw data processed by this model. Based on this estimation, the plant growth stages are analyzed by the comfort ratio. The different growth stages, light conditions and time frames are considered for determining the reference borders for categorizing the variation in each parameter. The microclimate parameters can be assessed dynamically with comfort ratio index as the indicator when multiple greenhouses are considered. The crop growth environment is interpreted better with the Simulink model and IoT sensor nodes. The result of supervised learning leads to improved efficiency in crop production developing optimal control strategies in the greenhouse environment.

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Zhao, Xia, Xingchuan Wang, Guangchao Cao, Kelong Chen, Wenjia Tang, and Zhijun Zhang. "Crop Identification by Using Seasonal Parameters Extracted from Time Series Landsat Images in a Mountainous Agricultural County of Eastern Qinghai Province, China." Journal of Agricultural Science 9, no. 4 (March 14, 2017): 116. http://dx.doi.org/10.5539/jas.v9n4p116.

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Time series vegetable indexes (Vis) have been evidenced a useful data to extract vegetable phenology and identify crop types. This paper conducted such a research in Qinghai Province by using Landsat TM images, via four steps, i) sampling single-crop plots and extracting crop spectrums based on pure signle-crop pixels; ii) building time-series vegetable indexes by using Landsat 8 TM images (2013-2014); iii) extracting seasonal parameters according to algorithms defined in TIMESAT program; vi) generating a decision tree for identifying crop types and validate classification accuracy via ground investigation. The results indicate that crops planted in a larger continuous range, such as spring wheat, potato and rapeseed, achieved an acceptable accuracy of above 70%, while crops planted too dispersedly (like broad bean, which is often inter-planted with other crops) or with a too smaller planting range (like barley), remained a poor recognition rates (below 50%). The value of this work lies in it displayed not only the classification accuracy of crop types in this region by using such methodology, but also the feasibility of integrating VIs calculation, seasonal parameter extracting and decision tree generation into one computer program, which is highly desired in this region.

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Bahrami, Hazhir, Saeid Homayouni, Abdolreza Safari, Sayeh Mirzaei, Masoud Mahdianpari, and Omid Reisi-Gahrouei. "Deep Learning-Based Estimation of Crop Biophysical Parameters Using Multi-Source and Multi-Temporal Remote Sensing Observations." Agronomy 11, no. 7 (July 3, 2021): 1363. http://dx.doi.org/10.3390/agronomy11071363.

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Remote sensing data are considered as one of the primary data sources for precise agriculture. Several studies have demonstrated the excellent capability of radar and optical imagery for crop mapping and biophysical parameter estimation. This paper aims at modeling the crop biophysical parameters, e.g., Leaf Area Index (LAI) and biomass, using a combination of radar and optical Earth observations. We extracted several radar features from polarimetric Synthetic Aperture Radar (SAR) data and Vegetation Indices (VIs) from optical images to model crops’ LAI and dry biomass. Then, the mutual correlations between these features and Random Forest feature importance were calculated. We considered two scenarios to estimate crop parameters. First, Machine Learning (ML) algorithms, e.g., Support Vector Regression (SVR), Random Forest (RF), Gradient Boosting (GB), and Extreme Gradient Boosting (XGB), were utilized to estimate two crop biophysical parameters. To this end, crops’ dry biomass and LAI were estimated using three input data; (1) SAR polarimetric features; (2) spectral VIs; (3) integrating both SAR and optical features. Second, a deep artificial neural network was created. These input data were fed to the mentioned algorithms and evaluated using the in-situ measurements. These observations of three cash crops, including soybean, corn, and canola, have been collected over Manitoba, Canada, during the Soil Moisture Active Validation Experimental 2012 (SMAPVEX-12) campaign. The results showed that GB and XGB have great potential in parameter estimation and remarkably improved accuracy. Our results also demonstrated a significant improvement in the dry biomass and LAI estimation compared to the previous studies. For LAI, the validation Root Mean Square Error (RMSE) was reported as 0.557 m2/m2 for canola using GB, and 0.298 m2/m2 for corn using GB, 0.233 m2/m2 for soybean using XGB. RMSE was reported for dry biomass as 26.29 g/m2 for canola utilizing SVR, 57.97 g/m2 for corn using RF, and 5.00 g/m2 for soybean using GB. The results revealed that the deep artificial neural network had a better potential to estimate crop parameters than the ML algorithms.

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Zeng, Wenzhi, Yuchao Lu, Amit Kumar Srivastava, Thomas Gaiser, and Jiesheng Huang. "Parameter Sensitivity and Uncertainty of Radiation Interception Models for Intercropping System." Ecological Chemistry and Engineering S 27, no. 3 (September 1, 2020): 437–56. http://dx.doi.org/10.2478/eces-2020-0028.

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AbstractEstimating the interception of radiation is the first and crucial step for the prediction of production for intercropping systems. Determining the relative importance of radiation interception models to the specific outputs could assist in developing suitable model structures, which fit to the theory of light interception and promote model improvements. Assuming an intercropping system with a taller and a shorter crop, a variance-based global sensitivity analysis (EFAST) was applied to three radiation interception models (M1, M2 and M3). The sensitivity indices including main (Si) and total effects (STi) of the fraction of intercepted radiation by the taller (ftaller), the shorter (fshorter) and both intercrops together (fall) were quantified with different perturbations of the geometric arrangement of the crops (10-60 %). We found both ftaller and fshorter in M1 are most sensitive to the leaf area index of the taller crop (LAItaller). In M2, based on the main effects, the leaf area index of the shorter crop (LAIshorter) replaces LAItaller and becomes the most sensitive parameter for fshorter when the perturbations of widths of taller and shorter crops (Wtaller and Wshorter) become 40 % and larger. Furthermore, in M3, ftaller is most sensitive to LAItaller while fshorter is most sensitive to LAIshorter before the perturbations of geometry parameters becoming larger than 50 %. Meanwhile, LAItaller, LAIshorter, and Ktaller are the three most sensitive parameters for fall in all three models. From the results we conclude that M3 is the most plausible radiation interception model among the three models.

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Wallach, Daniel, Bruno Goffinet, and Marie Tremblay. "Parameter estimation in crop models: exploring the possibility of estimating linear combinations of parameters." Agronomie 22, no. 2 (March 2002): 171–78. http://dx.doi.org/10.1051/agro:2002004.

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Kremer, Cristian, Carlos Faúndez, Víctor Beyá-Marshall, Nicolas Franck, and Víctor Muñoz-Aravena. "Transpiration-use efficiency of young cactus pear plants (Opuntia ficus-indica L.)." International Journal of Agriculture and Natural Resources 48, no. 2 (2021): 115–24. http://dx.doi.org/10.7764/ijanr.v48i2.2255.

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Opuntia ficus-indica is a versatile crop that is resilient to drought, making it perfect for semiarid to arid zones. However, the lack of knowledge associated with its benefits and the lack of simple crop growth simulation models to determine its potential development, among others, has prevented its expansion. Transpiration-use efficiency (w) has been used to evaluate crop performance under different water supplies; however, the lack of consistency in w values under different environmental conditions has impeded its use as a transferable parameter. To overcome this problem, w is estimated through the normalized water-use efficiency (kDa) and the vapor pressure deficit (Da) as w = kDa Da-1, where kDa is a crop-dependent parameter. Therefore, the goals of this research were (i) to determine w and kDa in young plants of Opuntia ficus-indica and (ii) to compare the obtained parameters with values from other species. The w and kDa results were 18.57 (g kg-1) and 6.48 (g kPa kg-1), respectively. Here, w was more than two to six times the value for traditional cereals (maize, rice, wheat), while kDa was larger than that of most C3 crops and fell in the range for C4 and CAM crops. This is the first study that explicitly determines kDa for Opuntia ficus-indica; hence, more research should be carried out on its estimation, including under different agroclimatic conditions and in later stages of development. As a first approximation, the parameters obtained here can be used as a simple model to estimate yield projections of Opuntia ficus-indica.

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Nantasaksiri, Kotchakarn, Patcharawat Charoen-Amornkitt, and Takashi Machimura. "Land Potential Assessment of Napier Grass Plantation for Power Generation in Thailand Using SWAT Model. Model Validation and Parameter Calibration." Energies 14, no. 5 (March 1, 2021): 1326. http://dx.doi.org/10.3390/en14051326.

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In Thailand, Napier grass is expected to play an important role as an energy resource for future power generation. To accomplish this goal, numerous areas are required for Napier grass plantations. Before introducing crops, the land potential of the country and the impact of crops on the environment should be assessed. The soil and water assessment tool (SWAT) model is very useful in investigating crop impacts and land potential. Unfortunately, the crop growth parameters of Napier grass are yet to be identified and, thus, conducting effective analysis has not been possible. Accordingly, in this study, parameter calibration and SWAT model validation of Napier grass production in Thailand was carried out using datasets from eight sites with 93 samples. Parameter sensitivity analysis was performed prior to parameter calibration, the results of which suggest that the radiation use efficiency and potential harvested index are both highly sensitive. The crop growth parameters were calibrated in order of their sensitivity index ranking, and the final values were obtained by reducing the root mean square error from 10.77 to 1.38 t·ha−1. The validation provides satisfactory results with coefficient of determination of 0.951 and a mean error of 0.321 t·ha−1. Using the developed model and calibrated parameters, local Napier grass dry matter yield can be evaluated accurately. The results reveal that, if only abandoned area in Thailand is used, then Napier grass can provide roughly 33,600–44,900 GWh of annual electricity, and power plant carbon dioxide (CO2) emissions can be reduced by approximately 21.2–28.3 Mt-CO2. The spatial distribution of estimated yield obtained in this work can be further utilized for land suitability analysis to help identify locations for Napier grass plantations, anaerobic digesters, and biogas power plants.

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Reisi Gahrouei, O., S. Homayouni, and A. Safari. "ESTIMATING CANOLA’S BIOPHYSICAL PARAMETERS FROM TEMPORAL, SPECTRAL, AND POLARIMETRIC IMAGERY USING MACHINE LEARNING APPROACHES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W18 (October 19, 2019): 885–89. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w18-885-2019.

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Abstract. The objective of this study was to investigate the application of multi-temporal optical and polarimetric synthetic aperture radar (PolSAR) Earth observations for crop characterization. Crop dry biomass, Leaf Area Index (LAI), and Plant Water Content (PWC) were estimated and assessed using Machin learning approaches. An accurate estimation of crop parameters provides essential information to increased food production and plays a crucial role in the management of agricultural lands. Multispectral and PolSAR data provide valuable observations of spectral and structural properties which are essential for crops parameter modelling. The Earth observations used in this paper were collected by RapidEye satellites and Unmanned Aerial Vehicle Synthetic Aperture Radar (UAVSAR) system in the summer of 2012, over an agriculture area in Winnipeg, Manitoba, Canada. The RapidEye vegetation indices (VIs) and UAVSAR polarimetric parameters were used as inputs in artificial neural network (ANN) and support vector regression (SVR) models for canola biophysical parameters estimation. The best models were provided by SVR for canola. Also combining optical VIs and polarimetric features appeared as a powerful tool for crop parameters estimation in agricultural lands.

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CLOUTIS, E. A., D. R. CONNERY, D. J. MAJOR, and F. J. DOVER. "Airborne multi-spectral monitoring of agricultural crop status: effect of time of year, crop type and crop condition parameter." International Journal of Remote Sensing 17, no. 13 (September 1996): 2579–601. http://dx.doi.org/10.1080/01431169608949094.

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de Souza, Romina, M. Teresa Peña-Fleitas, Rodney B. Thompson, Marisa Gallardo, and Francisco M. Padilla. "Assessing Performance of Vegetation Indices to Estimate Nitrogen Nutrition Index in Pepper." Remote Sensing 12, no. 5 (February 26, 2020): 763. http://dx.doi.org/10.3390/rs12050763.

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Vegetation indices (VIs) can be useful tools to evaluate crop nitrogen (N) status. To be effective, VIs measurements must be related to crop N status. The nitrogen nutrition index (NNI) is a widely accepted parameter of crop N status. The present work evaluates the performance of several VIs to estimate NNI in sweet pepper (Capsicum annuum). The performance of VIs to estimate NNI was evaluated using parameters of linear regression analysis conducted for calibration and validation. Three different sweet pepper crops were grown with combined irrigation and fertigation, in Almería, Spain. In each crop, five different N concentrations in the nutrient solution were frequently applied by drip irrigation. Proximal crop reflectance was measured with Crop Circle ACS470 and GreenSeeker handheld sensors, approximately every ten days, throughout the crops. The relative performance of VIs differed between phenological stages. Relationships of VIs with NNI were strongest in the early fruit growth and flowering stages, and less strong in the vegetative and harvest stages. The green band-based VIs, GNDVI, and GVI, provided the best results for estimating crop NNI in sweet pepper, for individual phenological stages. GNDVI had the best performance in the vegetative, flowering, and harvest stages, and GVI had the best performance in the early fruit growth stage. Some of the VIs evaluated are promising tools to estimate crop N status in sweet pepper and have the potential to contribute to improving crop N management of sweet pepper crops.

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McCullagh, Peter, and David Clifford. "Evidence for conformal invariance of crop yields." Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 462, no. 2071 (February 28, 2006): 2119–43. http://dx.doi.org/10.1098/rspa.2006.1667.

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The aim of this paper is to study the nature of spatial correlation of yields of agricultural crops. The focus is primarily on natural or non-anthropogenic spatial variation, patterns that cannot be explained by topography, by variety or treatment effects, or by agricultural practices. Conformal invariance implies stationarity and isotropy, and also determines the rate of decay of spatial correlations. The resulting Gaussian model is studied empirically to see whether it describes satisfactorily the pattern of spatial correlations observed in field trials of various crops. By embedding the law in a larger statistical model, a convolution of white noise and the Matérn class having a range parameter λ −1 and a smoothness parameter ν , and by gathering data of sufficient range and quantity, the model predictions were tested. Twenty-five examples of crop yields are studied, including cereals, root crops and other vegetables, nut, citrus and alfalfa yields. At the scale of typical field trials, we find that non-anthropogenic variation is reasonably close to isotropic. Furthermore, we find consistent evidence that the range parameter tends to be large and the smoothness parameter small. The large value of the range parameter confirms Fairfield Smith (Fairfield Smith 1938 J. Agric. Sci. 28 , 1–23), who found that spatial correlation in agricultural processes decreases with distance, but at a slower rate than exponential. The small value of the smoothness parameter means that, by Matérn standards, agricultural processes are rough. For each of the examples studied, the limiting model fits the data just as well as the full model, in reasonable agreement with the hypothesis of the conformal model that ( λ , ν )=(0, 0) for all crops in all seasons.

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Tomíček, Jiří, Jan Mišurec, and Petr Lukeš. "Prototyping a Generic Algorithm for Crop Parameter Retrieval across the Season Using Radiative Transfer Model Inversion and Sentinel-2 Satellite Observations." Remote Sensing 13, no. 18 (September 13, 2021): 3659. http://dx.doi.org/10.3390/rs13183659.

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In this study, Sentinel-2 data were used for the retrieval of three key biophysical parameters of crops: leaf area index (LAI), leaf chlorophyll content (LCC), and leaf water content (LWC) for dominant crop types in the Czech Republic, including winter wheat (Triticum aestivum), spring barley (Hordeum vulgare), winter rapeseed (Brassica napus subsp. napus), alfalfa (Medicago sativa), sugar beet (Beta vulgaris), and corn (Zea mays subsp. Mays) in different stages of crop development. Artificial neural networks were applied in combination with an approach using look-up tables that is based on PROSAIL simulations to retrieve the biophysical properties tailored for each crop type. Crop-specific PROSAIL model optimization and validation were based upon a large dataset of in situ measurements collected in 2017 and 2018 in lowland of Central Bohemia region. For LCC and LAI, respectively, low relative root mean square error (rRMSE; 25%, 37%) was achieved. Additionally, a relatively strong correlation with in situ measurements (r = 0.80) was obtained for LAI. On the contrary, the results of the LWC parameter retrieval proved to be unsatisfactory. We have developed a generic tool for biophysical monitoring of agricultural crops based on the interpretation of Sentinel-2 satellite data by inversion of the radiation transfer model. The resulting crop condition maps can serve as precision agriculture inputs for selective fertilizer and irrigation application as well as for yield potential assessment.

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Acharya, Subodh, Melanie Correll, James W. Jones, Kenneth J. Boote, Phillip D. Alderman, Zhengjun Hu, and C. Eduardo Vallejos. "Reliability of Genotype-Specific Parameter Estimation for Crop Models: Insights from a Markov Chain Monte-Carlo Estimation Approach." Transactions of the ASABE 60, no. 5 (2017): 1699–712. http://dx.doi.org/10.13031/trans.12183.

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Abstract. Parameter estimation is a critical step in successful application of dynamic crop models to simulate crop growth and yield under various climatic and management scenarios. Although inverse modeling parameterization techniques significantly improve the predictive capabilities of models, whether these approaches can recover the true parameter values of a specific genotype or cultivar is seldom investigated. In this study, we applied a Markov Chain Monte-Carlo (MCMC) method to the DSSAT dry bean model to estimate (recover) the genotype-specific parameters (GSPs) of 150 synthetic recombinant inbred lines (RILs) of dry bean. The synthetic parents of the population were assigned contrasting GSP values obtained from a database, and each of these GSPs was associated with several quantitative trait loci. A standard inverse modeling approach that simultaneously estimated all GSPs generated a set of values that could reproduce the original synthetic observations, but many of the estimated GSP values significantly differed from the original values. However, when parameter estimation was carried out sequentially in a stepwise manner, according to the genetically controlled plant development process, most of the estimated parameters had values similar to the original values. Developmental parameters were more accurately estimated than those related to dry mass accumulation. This new approach appears to reduce the problem of equifinality in parameter estimation, and it is especially relevant if attempts are made to relate parameter values to individual genes. Keywords: Crop models, Equifinality, Genotype-specific parameters, Markov chain Monte-Carlo, Parameterization.

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Singha, Chiranjit, Kishore Chandra Swain, and Sanjay Kumar Swain. "Best Crop Rotation Selection with GIS-AHP Technique Using Soil Nutrient Variability." Agriculture 10, no. 6 (June 9, 2020): 213. http://dx.doi.org/10.3390/agriculture10060213.

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Crop selections and rotations are very important in optimising land and labour productivities, enhancing higher cropping intensities, producing better crop yield. A land suitability analysis system based on the analytical hierarchy process (AHP) technique coupled with the Geographic Information System (GIS) software environment can be a unique tool for better crop selection. The AHP-GIS technique was used in land suitability analysis in crop rotation decisions, for rice-jute (Kharif season) and potato-lentil (Rabi season) crops in the Hooghly District, West Bengal, India. The study area covering 291 ha was classified based on eight major soil nutrient levels with 70 randomly selected plots for soil sampling and analysis. The soil nutrient variability was examined with descriptive statistics followed by best semivariogram-based model selection for kriging interpolation in the ‘R’ software environment. The pairwise comparison matrix based ranking of parameters and giving weights was carried out considering the importance of each parameter for specific crops. The total area, being under the major rice-potato belt, could be classified from the suitability view point to the ‘highly suitable’(S1) class occupying 29.2%, and ‘not suitable’ (N) class; 4.5% for rice, about 6.5% of land is ‘highly suitable’ (S1), ‘and nearly 2.1% area is ‘not suitable’ (N) for jute; and 21.3% is ‘highly suitable’ (S1) for potato and 12.4% for lentil crops. The yield maps showed nearly 75% and 90% of the area for rice and potato crops, respectively, gave sound crop yield. Furthermore, the GIS platform was used for crop rotation analysis to spread multiple seasons ensuring better crop management in long run. Overall, 25% of the rice crop area for jute in Kharif and 8% of potato crop area for lentil in the Rabi season were recommended as replacements.

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JOLLIFFE, PETER A., and FREDRICK M. WANJAU. "Competition and productivity in crop mixtures: some properties of productive intercrops." Journal of Agricultural Science 132, no. 4 (June 1999): 425–35. http://dx.doi.org/10.1017/s0021859699006450.

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Inverse yield–density relationships were used to evaluate how competitive balances in mixed plant species associations may influence productivity, as measured by three indices : Relative Yield Total (RYT), Relative Land Output (RLO), and Total Land Output (TLO). Parameters of the inverse relationships, which express competitive influences and plant growth potential in the absence of competition, were incorporated into expressions used to predict RYT, RLO and TLO. Initial parameter values were derived from 25 experiments on binary species mixtures, and parameter values were systematically varied over a 100-fold range. Response to parameter manipulation was also investigated in five specific binary associations representing a broad range of relative mixture productivity. As indicated by RLO or RYT, and in accord with ecological concepts of niche differentiation, high productivity of mixtures relative to monocultures occurred if between-species competition was low. As indicated by TLO, the total combined productivity of the mixed species was enhanced by higher potential growth per plant in the absence of competition. Lower within- and between-species competition also increased TLO. There was a significant positive correlation between RYT and RLO. Relative and total measures of mixture productivity, however, showed different responses to parameter manipulation, and were not correlated.

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Heuer, A. I. "Studying Parameter Sensitivity and Behaviour of the Crop Model STICS." Open Hydrology Journal 5, no. 1 (May 30, 2011): 58–68. http://dx.doi.org/10.2174/1874378101105010058.

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Rillig, Matthias C., and Anika Lehmann. "Exploring the agricultural parameter space for crop yield and sustainability." New Phytologist 223, no. 2 (March 7, 2019): 517–19. http://dx.doi.org/10.1111/nph.15744.

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VIVEKANAND, VIVEKANAND, VINOD KUMAR, VIJAY KUMAR SINGH, and BHASKAR PRATAP SINGH. "Weather parameter based crop planning in Tarai region of Uttarakhand." INTERNATIONAL JOURNAL OF AGRICULTURAL ENGINEERING 10, no. 2 (October 15, 2017): 360–66. http://dx.doi.org/10.15740/has/ijae/10.2/360-366.

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Fang, Quanxiao, L. Ma, R. D. Harmel, Q. Yu, M. W. Sima, P. N. S. Bartling, R. W. Malone, B. T. Nolan, and J. Doherty. "Uncertainty of CERES-Maize Calibration under Different Irrigation Strategies Using PEST Optimization Algorithm." Agronomy 9, no. 5 (May 10, 2019): 241. http://dx.doi.org/10.3390/agronomy9050241.

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An important but rarely studied aspect of crop modeling is the uncertainty associated with model calibration and its effect on model prediction. Biomass and grain yield data from a four-year maize experiment (2008–2011) with six irrigation treatments were divided into subsets by either treatments (Calibration-by-Treatment) or years (Calibration-by-Year). These subsets were then used to calibrate crop cultivar parameters in CERES (Crop Environment Resource Synthesis)-Maize implemented within RZWQM2 (Root Zone Water Quality Model 2) using the automatic Parameter ESTimation (PEST) algorithm to explore model calibration uncertainties. After calibration for each subset, PEST also generated 300 cultivar parameter sets by assuming a normal distribution of each parameter within their reported values in the literature, using the Latin hypercube sampling (LHS) method. The parameter sets that produced similar goodness of fit (11–164 depending on subset used for calibration) were then used to predict all the treatments and years of the entire dataset. Our results showed that the selection of calibration datasets greatly affected the calibrated crop parameters and their uncertainty, as well as prediction uncertainty of grain yield and biomass. The high variability in model prediction of grain yield and biomass among the six (Calibration-by-Treatment) or the four (Calibration-by-Year) scenarios indicated that parameter uncertainty should be considered in calibrating CERES-Maize with grain yield and biomass data from different irrigation treatments, and model predictions should be provided with confidence intervals.

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Sun, Jun, Guo Qing Zhang, Zhuang Chen, Lei Gao, and Jun Ming Li. "Cultivation Management and Standard Library Automatic Query System of Digital Greenhouse." Applied Mechanics and Materials 190-191 (July 2012): 64–69. http://dx.doi.org/10.4028/www.scientific.net/amm.190-191.64.

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The research and applied technology about agriculture are attracting increasing attention, and especially digital greenhouses have become an important part of efficient agriculture. In this paper, basing on the environmental factors and crops growth status information, management system sorts out, analyzes and summarizes the data to give the best environment parameters for different crops in different external environment and to give the best growth parameter value for crop in different growth stages. A standard database of digital greenhouse will be built in this paper. Basing on the database, automatic identification and query model will be built. The system will provide daily operation schedule for crops during the whole growth of crops. And all kinds of growth parameters information in the process of crops growth will be analyzed by the system. Then the system compares the crop growth situation with standard database and sends the abnormal information to users timely. Simultaneously, real-time video monitoring can be used. Some daily routine work information can be recorded and suggested, such as watering, fertilizing and weeding etc. Based on crops growth and development patterns, the environment conditions are controlled and high quality, high yield, and efficient cultivation of plants will be realized.

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Challinor, A. J., T. R. Wheeler, J. M. Slingo, and D. Hemming. "Quantification of physical and biological uncertainty in the simulation of the yield of a tropical crop using present-day and doubled CO 2 climates." Philosophical Transactions of the Royal Society B: Biological Sciences 360, no. 1463 (October 24, 2005): 2085–94. http://dx.doi.org/10.1098/rstb.2005.1740.

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The impacts of climate change on crop productivity are often assessed using simulations from a numerical climate model as an input to a crop simulation model. The precision of these predictions reflects the uncertainty in both models. We examined how uncertainty in a climate (HadAM3) and crop General Large-Area Model (GLAM) for annual crops model affects the mean and standard deviation of crop yield simulations in present and doubled carbon dioxide (CO 2 ) climates by perturbation of parameters in each model. The climate sensitivity parameter ( λ , the equilibrium response of global mean surface temperature to doubled CO 2 ) was used to define the control climate. Observed 1966–1989 mean yields of groundnut ( Arachis hypogaea L.) in India were simulated well by the crop model using the control climate and climates with values of λ near the control value. The simulations were used to measure the contribution to uncertainty of key crop and climate model parameters. The standard deviation of yield was more affected by perturbation of climate parameters than crop model parameters in both the present-day and doubled CO 2 climates. Climate uncertainty was higher in the doubled CO 2 climate than in the present-day climate. Crop transpiration efficiency was key to crop model uncertainty in both present-day and doubled CO 2 climates. The response of crop development to mean temperature contributed little uncertainty in the present-day simulations but was among the largest contributors under doubled CO 2 . The ensemble methods used here to quantify physical and biological uncertainty offer a method to improve model estimates of the impacts of climate change.

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Bilionis, I., B. A. Drewniak, and E. M. Constantinescu. "Crop physiology calibration in the CLM." Geoscientific Model Development 8, no. 4 (April 15, 2015): 1071–83. http://dx.doi.org/10.5194/gmd-8-1071-2015.

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Abstract. Farming is using more of the land surface, as population increases and agriculture is increasingly applied for non-nutritional purposes such as biofuel production. This agricultural expansion exerts an increasing impact on the terrestrial carbon cycle. In order to understand the impact of such processes, the Community Land Model (CLM) has been augmented with a CLM-Crop extension that simulates the development of three crop types: maize, soybean, and spring wheat. The CLM-Crop model is a complex system that relies on a suite of parametric inputs that govern plant growth under a given atmospheric forcing and available resources. CLM-Crop development used measurements of gross primary productivity (GPP) and net ecosystem exchange (NEE) from AmeriFlux sites to choose parameter values that optimize crop productivity in the model. In this paper, we calibrate these parameters for one crop type, soybean, in order to provide a faithful projection in terms of both plant development and net carbon exchange. Calibration is performed in a Bayesian framework by developing a scalable and adaptive scheme based on sequential Monte Carlo (SMC). The model showed significant improvement of crop productivity with the new calibrated parameters. We demonstrate that the calibrated parameters are applicable across alternative years and different sites.

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Bilionis, I., B. A. Drewniak, and E. M. Constantinescu. "Crop physiology calibration in CLM." Geoscientific Model Development Discussions 7, no. 5 (October 14, 2014): 6733–71. http://dx.doi.org/10.5194/gmdd-7-6733-2014.

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Abstract. Farming is using more terrestrial ground, as population increases and agriculture is increasingly used for non-nutritional purposes such as biofuel production. This agricultural expansion exerts an increasing impact on the terrestrial carbon cycle. In order to understand the impact of such processes, the Community Land Model (CLM) has been augmented with a CLM-Crop extension that simulates the development of three crop types: maize, soybean, and spring wheat. The CLM-Crop model is a complex system that relies on a suite of parametric inputs that govern plant growth under a given atmospheric forcing and available resources. CLM-Crop development used measurements of gross primary productivity and net ecosystem exchange from AmeriFlux sites to choose parameter values that optimize crop productivity in the model. In this paper we calibrate these parameters for one crop type, soybean, in order to provide a faithful projection in terms of both plant development and net carbon exchange. Calibration is performed in a Bayesian framework by developing a scalable and adaptive scheme based on sequential Monte Carlo (SMC).

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Yang, Chenyao, Christoph Menz, Helder Fraga, Samuel Reis, Nelson Machado, Aureliano C. Malheiro, and João A. Santos. "Simultaneous Calibration of Grapevine Phenology and Yield with a Soil–Plant–Atmosphere System Model Using the Frequentist Method." Agronomy 11, no. 8 (August 20, 2021): 1659. http://dx.doi.org/10.3390/agronomy11081659.

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Reliable estimations of parameter values and associated uncertainties are crucial for crop model applications in agro-environmental research. However, estimating many parameters simultaneously for different types of response variables is difficult. This becomes more complicated for grapevines with different phenotypes between varieties and training systems. Our study aims to evaluate how a standard least square approach can be used to calibrate a complex grapevine model for simulating both the phenology (flowering and harvest date) and yield of four different variety–training systems in the Douro Demarcated Region, northern Portugal. An objective function is defined to search for the best-fit parameters that result in the minimum value of the unweighted sum of the normalized Root Mean Squared Error (nRMSE) of the studied variables. Parameter uncertainties are estimated as how a given parameter value can determine the total prediction variability caused by variations in the other parameter combinations. The results indicate that the best-estimated parameters show a satisfactory predictive performance, with a mean bias of −2 to 4 days for phenology and −232 to 159 kg/ha for yield. The corresponding variance in the observed data was generally well reproduced, except for one occasion. These parameters are a good trade-off to achieve results close to the best possible fit of each response variable. No parameter combinations can achieve minimum errors simultaneously for phenology and yield, where the best fit to one variable can lead to a poor fit to another. The proposed parameter uncertainty analysis is particularly useful to select the best-fit parameter values when several choices with equal performance occur. A global sensitivity analysis is applied where the fruit-setting parameters are identified as key determinants for yield simulations. Overall, the approach (including uncertainty analysis) is relatively simple and straightforward without specific pre-conditions (e.g., model continuity), which can be easily applied for other models and crops. However, a challenge has been identified, which is associated with the appropriate assumption of the model errors, where a combination of various calibration approaches might be essential to have a more robust parameter estimation.

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P. Chothani, Ekta, H. J. Kapadiya, M. F. Acharya, and C. M. Bhaliya. "Impact of Weather Parameter on Early Blight Epidemiology in Tomato Crop." International Journal of Current Microbiology and Applied Sciences 6, no. 11 (November 10, 2017): 3160–66. http://dx.doi.org/10.20546/ijcmas.2017.611.370.

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P. N. Singh, J. P. Mitchell, and W. W. Wallender. "Parameter Optimization for Predicting Soil Water Movement under Crop Residue Cover." Transactions of the ASABE 54, no. 6 (2011): 2029–35. http://dx.doi.org/10.13031/2013.40661.

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Garcia, A., and R. G. B. André. "ANALYSIS OF THE PRIESTLEY-TAYLOR ALPHA PARAMETER FOR A BEAN CROP." Acta Horticulturae, no. 537 (October 2000): 151–57. http://dx.doi.org/10.17660/actahortic.2000.537.15.

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Moeckel, Thomas, Supriya Dayananda, Rama Nidamanuri, Sunil Nautiyal, Nagaraju Hanumaiah, Andreas Buerkert, and Michael Wachendorf. "Estimation of Vegetable Crop Parameter by Multi-temporal UAV-Borne Images." Remote Sensing 10, no. 5 (May 22, 2018): 805. http://dx.doi.org/10.3390/rs10050805.

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KUMAR, DEWENDRA, R. K. NAIK, P. K. NISHAD, and P. R. SAHU. "Optimization of crop-machine parameter on the performance of Kodo pearler." INTERNATIONAL JOURNAL OF AGRICULTURAL ENGINEERING 10, no. 2 (October 15, 2017): 545–49. http://dx.doi.org/10.15740/has/ijae/10.2/545-549.

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Dai, Chunni, Meng Yao, Zhujie Xie, Chunhong Chen, and Jingao Liu. "Parameter optimization for growth model of greenhouse crop using genetic algorithms." Applied Soft Computing 9, no. 1 (January 2009): 13–19. http://dx.doi.org/10.1016/j.asoc.2008.02.002.

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Djaby, Bakary, Allard De Wit, Louis Kouadio, Moussa El Jarroudi, and Bernard Tychon. "Spatial Distribution of Calibrated WOFOST Parameters and Their Influence on the Performances of a Regional Yield orecasting System." Sustainable Agriculture Research 2, no. 4 (July 15, 2013): 12. http://dx.doi.org/10.5539/sar.v2n4p12.

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We investigate in this study (i) a redefinition of crop variety zonations at a spatial scale of 10x10 km, and (ii) the influence of recalibrated crop parameters on regional yield forecasting of winter wheat and grain maize in western Europe. The baseline zonation and initial crop parameter set was derived from the operational European crop growth monitoring system (CGMS) which involves the agrometeorological model WOFOST. Air temperature data from 325 weather stations over the 1992-2007 period were used to define new zonations in a 300 x 300 km test site. Two parameters which influenced mostly the phenological development stages (i.e. TSUM1 and TSUM2, the effective air temperature sums from emergence to anthesis, and from anthesis to maturity, respectively) were chosen and calibrated. The CGMS was finally run based on these new recalibrated parameters and simulated crop status indicators were compared with official statistics over the 2000-2007 period. Our results showed that the days of anthesis and maturity were simulated with coefficients of determination (R2) ranging from 0.22 to 0.87 for both crops over the study site. A qualitative assessment of maximum leaf area index and harvest index also revealed a more consistent spatial pattern than the initial zonation in the simulation results. Finally, recalibrated TSUM1 and TSUM2 led to improved relationships between official yield and simulated crop indicators (significant R2 in 17 out of 28 and in 14 out of 59 NUTS3 regions with respect to the best predictor for grain maize and winter wheat, respectively).

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Rezaei, M., P. Seuntjens, I. Joris, W. Boënne, S. Van Hoey, P. Campling, and W. M. Cornelis. "Sensitivity of water stress in a two-layered sandy grassland soil to variations in groundwater depth and soil hydraulic parameters." Hydrology and Earth System Sciences 20, no. 1 (January 29, 2016): 487–503. http://dx.doi.org/10.5194/hess-20-487-2016.

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Abstract. Monitoring and modelling tools may improve irrigation strategies in precision agriculture. We used non-invasive soil moisture monitoring, a crop growth and a soil hydrological model to predict soil water content fluctuations and crop yield in a heterogeneous sandy grassland soil under supplementary irrigation. The sensitivity of the soil hydrological model to hydraulic parameters, water stress, crop yield and lower boundary conditions was assessed after integrating models. Free drainage and incremental constant head conditions were implemented in a lower boundary sensitivity analysis. A time-dependent sensitivity analysis of the hydraulic parameters showed that changes in soil water content are mainly affected by the soil saturated hydraulic conductivity Ks and the Mualem–van Genuchten retention curve shape parameters n and α. Results further showed that different parameter optimization strategies (two-, three-, four- or six-parameter optimizations) did not affect the calculated water stress and water content as significantly as does the bottom boundary. In this case, a two-parameter scenario, where Ks was optimized for each layer under the condition of a constant groundwater depth at 135–140 cm, performed best. A larger yield reduction, and a larger number and longer duration of stress conditions occurred in the free drainage condition as compared to constant boundary conditions. Numerical results showed that optimal irrigation scheduling using the aforementioned water stress calculations can save up to 12–22 % irrigation water as compared to the current irrigation regime. This resulted in a yield increase of 4.5–6.5 %, simulated by the crop growth model.

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Wan, Xingyu, Qingxi Liao, Yajun Jiang, and Yitao Liao. "Cattle Feeding Experiment and Chopping Device Parameter Determination for Mechanized Harvesting of Forage Rape Crop." Transactions of the ASABE 64, no. 2 (2021): 715–25. http://dx.doi.org/10.13031/trans.14341.

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HighlightsForage rape crop could effectively alleviate the lack of green forage for livestock in winter.With the growth of forage rape crop, stem lignification was exacerbated and its palatability degenerated.The relationship between particle length and palatability was explored in a cattle feeding experiment.Optimal working parameters of the chopping device were obtained for harvesting the crop in different stages.Abstract. Forage rape crop, which uses the immature plant leaf and stem of a hybrid rape crop (Brassica napus L.) with low erucic acid and glucosinolate to feed livestock, is an innovative fresh-fed feed material with the advantages of high yield, low cost, rich nutrients, and vigorous growth in winter. In this work, a systematic study was carried out on the relationships among the characteristics of forage rape crop stems, chopping device parameters of the harvester, feeding performance, and chopped particle length (PL) in different growth stages. The results of the stem characteristics tests indicated that stem lignification occurred and increased with growth of the crop from the bolting stage to the silique stage, leading to degeneration of its palatability. The cattle feeding experiment showed that when the bolting rape crop was used, the average feed intake of the cattle fed the chopped rape crop increased by 33.35%, compared to feeding the whole crop without chopping, while the average feeding time decreased by 35.44%. Further experiments on the effects of PL after chopping on feeding performance in different growth stages showed that the optimal PL values in the bolting, flowering, and silique stages were 80, 60, and 30 mm, respectively. Finally, the corresponding cutterhead rotational speeds of the chopping device were calculated as 450, 510, and 1200 r min-1, respectively. This study provides a reference for the development and application of harvesting equipment for forage rape crop. Keywords: Agricultural mechanization, Cattle feeding, Forage palatability, Harvester, Parameter matching.

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Cousens, R. "An empirical model relating crop yield to weed and crop density and a statistical comparison with other models." Journal of Agricultural Science 105, no. 3 (December 1985): 513–21. http://dx.doi.org/10.1017/s0021859600059396.

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SUMMARYA hyperbolic model relating crop yield to weed density is extended to include crop density as a further variable. Other models were obtained from published sources, eight being originally applied to yield of above-ground biomass and six to marketable yield. Data were obtained from a field experiment in which spring wheat and spring barley were planted either in monoculture or together and at a range of densities. Further data were obtained from a published experiment on Sinapis alba and barley grown in containers. The models were fitted to data using maximum likelihood estimation. Comparisons of residual sums of squares showed that for the wheat and barley field experiment biomass yield and marketable yield were sufficiently described by a three-parameter model. The Baeumer & de Wit (1968) equation proposed for replacement series experimental designs is considered reasonable for the analysis of field additive designs provided the parameters are interpreted in agronomic terms. For the Sinapis alba and barley experiment more complex models could be justified.

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Rezaei, M., P. Seuntjens, I. Joris, W. Boënne, S. Van Hoey, P. Campling, and W. M. Cornelis. "Sensitivity of water stress in a two-layered sandy grassland soil to variations in groundwater depth and soil hydraulic parameters." Hydrology and Earth System Sciences Discussions 12, no. 7 (July 20, 2015): 6881–920. http://dx.doi.org/10.5194/hessd-12-6881-2015.

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Abstract. Monitoring and modeling tools may improve irrigation strategies in precision agriculture. We used non-invasive soil moisture monitoring, a crop growth and a soil hydrological model to predict soil-water content fluctuations and crop yield in a heterogeneous sandy grassland soil under supplementary irrigation. The sensitivity of the model to hydraulic parameters, water stress, crop yield and lower boundary conditions was assessed. Free drainage and incremental constant head conditions was implemented in a lower boundary sensitivity analysis. A time-dependent sensitivity analysis showed that changes in soil water content are mainly affected by the soil saturated hydraulic conductivity Ks and the Mualem-van Genuchten retention curve shape parameters n and α. Results further showed that different parameter optimization strategies (two-, three-, four- or six-parameter optimizations) did not affect the calculated water stress and water content as significantly as does the bottom boundary. For this case, a two-parameter scenario, where Ks was optimized for each layer under the condition of a constant groundwater depth at 135–140 cm, performed best. A larger yield reduction, and a larger number and longer duration of stress conditions occurred in the free drainage condition as compared to constant boundary conditions. Numerical results showed that optimal irrigation scheduling using the aforementioned water stress calculations can save up to 12–22 % irrigation water as compared to the current irrigation regime. This resulted in a yield increase of 4.5–6.5 %, simulated by crop growth model.

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Young, Bryan G., David J. Gibson, Karla L. Gage, Joseph L. Matthews, David L. Jordan, Micheal D. K. Owen, David R. Shaw, Stephen C. Weller, and Robert G. Wilson. "Agricultural Weeds in Glyphosate-Resistant Cropping Systems in the United States." Weed Science 61, no. 1 (March 2013): 85–97. http://dx.doi.org/10.1614/ws-d-12-00001.1.

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A segment of the debate surrounding the commercialization of genetically engineered (GE) crops, such as glyphosate-resistant (GR) crops, focuses on the theory that implementation of these traits is an extension of the intensification of agriculture that will further erode the biodiversity of agricultural landscapes. A large field-scale study was conducted in 2006 in the United States on 156 different field sites with a minimum 3-yr history of GR corn, cotton, or soybean in the cropping system. The impact of cropping system, crop rotation, frequency of using the GR crop trait, and several categorical variables on emerged weed density and diversity was analyzed. Species richness, evenness, Shannon's H′, proportion of forbs, erect growth habit, and C3species diversity were all greater in agricultural sites that lacked crop rotation or were in a continuous GR crop system. Rotating between two GR crops (e.g., corn and soybean) or rotating to a non-GR crop resulted in less weed diversity than a continuous GR crop. The composition of the weed flora was more strongly related to location (geography) than any other parameter. The diversity of weed flora in agricultural sites with a history of GR crop production can be influenced by several factors relating to the specific method in which the GR trait is integrated (cropping system, crop rotation, GR trait rotation), the specific weed species, and the geographical location. The finding that fields with continuous GR crops demonstrated greater weed diversity is contrary to arguments opposing the use of GE crops. These results justify further research to clarify the complexities of crops grown with herbicide-resistance traits, or more broadly, GE crops, to provide a more complete characterization of their culture and local adaptation.

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Tumusiime, Emmanuel, Brorsen B. Wade, Jagadeesh Mosali, Jim Johnson, James Locke, and Jon T. Biermacher. "Determining Optimal Levels of Nitrogen Fertilizer Using Random Parameter Models." Journal of Agricultural and Applied Economics 43, no. 4 (November 2011): 541–52. http://dx.doi.org/10.1017/s1074070800000067.

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The parameters of yield response functions can vary by year. Past studies usually assume yield functions are nonstochastic or “limited” stochastic. In this study, we estimate rye–ryegrass yield functions in which all parameters are random. The three functional forms considered are the linear response plateau, the quadratic, and the Spillman-Mitscherlich. Nonstochastic yield models are rejected in favor of stochastic parameter models. Quadratic functional forms fit the data poorly. Optimal nitrogen application recommendations are calculated for the linear response plateau and Spillman-Mitscherlich. The stochastic models lead to smaller recommended levels of nitrogen, but the economic benefits of using fully stochastic crop yield functions are small because expected profit functions are relatively flat for the stochastic yield functions. Stochastic crop yield functions provide a way of incorporating production, uncertainty into input decisions.

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Nasirzadehdizaji, Rouhollah, Fusun Balik Sanli, Saygin Abdikan, Ziyadin Cakir, Aliihsan Sekertekin, and Mustafa Ustuner. "Sensitivity Analysis of Multi-Temporal Sentinel-1 SAR Parameters to Crop Height and Canopy Coverage." Applied Sciences 9, no. 4 (February 15, 2019): 655. http://dx.doi.org/10.3390/app9040655.

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The Polarimetric Synthetic Aperture Radar technique has provided various opportunities and challenges in agricultural activities mainly on crop management. The aim of this study is to investigate the sensitivity of 10 parameters derived from multi-temporal Sentinel-1 Synthetic Aperture Radar (SAR) data, to crop height and canopy coverage (CC) of maize, sunflower, and wheat. The correlation coefficient values indicate a high correlation for maize during the early growing stage. The coefficient determinations (R2) of 0.82 and 0.81 indicate that there is a strong relationship between the maize height and SAR parameters including VV + VH and VV, respectively. The maize CC is well correlated with VV parameter (R2 = 0.73), but it is observed that at the later growing stage the correlation became weaker. This means that the sensitivity decreases with increasing vegetation cover growth. Compared to maize, the sensitivity of SAR parameters to wheat variables is often good at the early stage. However, the highest correlation with wheat height represented by Alpha (α) decomposition parameter (R2 = 0.67). The sunflower height has an insignificant correlation with the majority of SAR parameters and only VH polarization shows low sensitivity (R2 = 0.31). The sunflower CC shows relatively higher correlation with VV polarization (R2 = 0.46) at the early stage while no considerable correlation is observed at the later stage. It is found that Sentinel-1 has a high potential for estimation of crop height and CC of the maize as a broad-leaf crop. The same is not true for sunflower as another broad-leaf crop.

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Pook, Torsten, Manfred Mayer, Johannes Geibel, Steffen Weigend, David Cavero, Chris C. Schoen, and Henner Simianer. "Improving Imputation Quality in BEAGLE for Crop and Livestock Data." G3: Genes|Genomes|Genetics 10, no. 1 (November 1, 2019): 177–88. http://dx.doi.org/10.1534/g3.119.400798.

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Imputation is one of the key steps in the preprocessing and quality control protocol of any genetic study. Most imputation algorithms were originally developed for the use in human genetics and thus are optimized for a high level of genetic diversity. Different versions of BEAGLE were evaluated on genetic datasets of doubled haploids of two European maize landraces, a commercial breeding line and a diversity panel in chicken, respectively, with different levels of genetic diversity and structure which can be taken into account in BEAGLE by parameter tuning. Especially for phasing BEAGLE 5.0 outperformed the newest version (5.1) which in turn also lead to improved imputation. Earlier versions were far more dependent on the adaption of parameters in all our tests. For all versions, the parameter ne (effective population size) had a major effect on the error rate for imputation of ungenotyped markers, reducing error rates by up to 98.5%. Further improvement was obtained by tuning of the parameters affecting the structure of the haplotype cluster that is used to initialize the underlying Hidden Markov Model of BEAGLE. The number of markers with extremely high error rates for the maize datasets were more than halved by the use of a flint reference genome (F7, PE0075 etc.) instead of the commonly used B73. On average, error rates for imputation of ungenotyped markers were reduced by 8.5% by excluding genetically distant individuals from the reference panel for the chicken diversity panel. To optimize imputation accuracy one has to find a balance between representing as much of the genetic diversity as possible while avoiding the introduction of noise by including genetically distant individuals.

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Shastry, K. Aditya, H. A. Sanjay, and Abhijeeth Deshmukh. "A Parameter Based Customized Artificial Neural Network Model for Crop Yield Prediction." Journal of Artificial Intelligence 9, no. 1-3 (December 15, 2015): 23–32. http://dx.doi.org/10.3923/jai.2016.23.32.

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Hu, Jian Ming, Xiao He Guo, and Guang Hui Li. "Crop Growth Environment Parameter Measurement and Control System Based on ARM Framework." Applied Mechanics and Materials 734 (February 2015): 242–46. http://dx.doi.org/10.4028/www.scientific.net/amm.734.242.

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Agricultural environment monitoring is the basic function of intelligent greenhouses, it broke through the geographical, environmental, and climate conditions on the influence of the crops, it is of great significance to the agricultural modernization and intelligentization, and promotes the development of agricultural advancement and intelligentization. Agricultural environment monitoring system based on ARM framework,PC S3C6410 is used as main control chip, running under WinCE environment, a good human-computer interface is provided; C8051F120 microcontroller as the core of its lowercompute, it acquires environment parameters in a variety of ways and adjusts on the surrounding soil and the air environment, the whole system’s good controllability can be ensured.

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HU, S., and X. MO. "Prediction of crop productivity and evapotranspiration with two photosynthetic parameter regionalization methods." Journal of Agricultural Science 152, no. 1 (November 27, 2012): 119–33. http://dx.doi.org/10.1017/s0021859612000901.

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SUMMARYParameter regionalization is the foundation for the spatial application of an ecosystem model at the canopy level and has been improved greatly by remote sensing (RS). Photosynthetic rate is restricted by the carboxylation rate, which is limited by the activity of the enzyme Rubisco. By including RS normalized difference vegetation index (NDVI) and census data of grain yield at the county level in an ecosystem model (vegetation interface processes (VIP) model), the pattern of photosynthetic parameter Vcmax (maximum catalytic activity of Rubisco) of winter wheat was obtained and then used to simulate the wheat yield and evapotranspiration (ET) in the North China Plain (referred to as the Vcmax method). To evaluate its performance, the simulated yield and ET were compared with those derived by the leaf area index (LAI) method using the retrieved LAI from NDVI to drive the VIP model. The results showed that the Vcmax method performed better than the LAI method in highly productive fields, while the LAI method described the inter-annual variations of yield more favourably in fields with low productivity. Over the study area, average yield (4520 kg/ha) and seasonal ET (360 mm) simulated by the LAI method was slightly lower than those simulated using the Vcmax method (4730 kg/ha for yield and 372 mm for ET). Compared with the census data of yield, the relative root mean square error (RMSE) of grain yield with Vcmax method (0·17) was lower than that of the LAI method (0·20). In conclusion, the physical model with spatial Vcmax pattern from remote sensing is reliable for regional crop productivity prediction.

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Zhao, Shuailing, and Zhibin Zhang. "A new recognition of crop row based on its structural parameter model." IFAC-PapersOnLine 49, no. 16 (2016): 431–38. http://dx.doi.org/10.1016/j.ifacol.2016.10.079.

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César Trejo Zúñiga, Elmer, Irineo Lorenzo López Cruz, and Agustín Ruíz García. "Parameter estimation for crop growth model using evolutionary and bio-inspired algorithms." Applied Soft Computing 23 (October 2014): 474–82. http://dx.doi.org/10.1016/j.asoc.2014.06.023.

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Journal articles on the topic 'Crop parameter'

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